Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x1c2adfddef0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x1c2ae052320>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_images = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name='input_images')
    z_vect = tf.placeholder(tf.float32, shape=(None, z_dim), name='z_vect')
    learnrate = tf.placeholder(tf.float32, name='learnrate')
    return input_images, z_vect, learnrate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha=0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        # This has to work with any generic dataset that we throw in. Currently, we either have
        # MNIST, which is 28x28, single channel and CELEBA, which is 178x218, 3 channel
        # But we're in luck! The parent function PROMISED to provide us data as 28x28x3 channel Yay!
        # Discriminator goes from image to single binary value (real/not real)
        # Input layer is 28x28xn. Not a lot we can do with that from an "evenly divisible number of layers" sense
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1) # leaky relu
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True) # help with training
        relu2 = tf.maximum(alpha * bn2, bn2) # leaky relu
        # 7x7x128
        
        # Flatten it
        flat = tf.reshape(relu2, (-1, 7*7*128))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha=0.02
    # Following the logic in the DCGAN example, generator variables are set to reuse when it's not training
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*128)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 128))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x128 now
        
        x2 = tf.layers.conv2d_transpose(x1, 64, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x64 now
        
        x3 = tf.layers.conv2d_transpose(x2, 32, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 28x28x32 now
        
        # Output layer
        # A stride of 1 will keep the overall shape the same, we just need to set the number
        # of filters to the desired number of output channels
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')
        # 28x28x3 now
        
        out = tf.tanh(logits)
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [22]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    smooth = 0.2
    # Follows what we did for the DCGAN exercise
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    # Smoothing is really important. Wow. Check this out.
    # http://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1 - smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    # Neat reference about the beta parameters:
    # https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [13]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    image_channels = len(data_image_mode)
    image_height = data_shape[1]
    image_width = data_shape[2]
    print("Using images of size (",image_width, ", ",image_height, ", ",image_channels,")")
    
    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    steps = 0
    print_every = 10
    show_every = 100
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                
                # tanh, our generator activation function scales to the range -1 to 1, 
                # but the images are -0.5 to 0.5. We'll scale them correctly here
                batch_images *= 2.0
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
                # Added a second run of the generator optimizer following a reviewer suggestion. Seems fine.

                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
        print("Complete.")
        show_generator_output(sess, 25, input_z, image_channels, data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [23]:
batch_size = 32
z_dim = 784
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
import sys
sys.path.append('c:/Users/johnc/git/iamslackbot')
import iamSlackBot
slackToken = "xoxp-114484949522-185575048470-307086059681-34c79c922f7938001aaaeb761280382f"  # os.environ["SLACK_AP$
msg = "Training Done"
slackChannel = "@john.cameron"
iamsb = iamSlackBot.IAMSlackBot(slackToken, slackChannel)

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
iamsb.sendMessage(msg)
Using images of size ( 28 ,  28 ,  1 )
Epoch 1/2... Discriminator Loss: 4.0009... Generator Loss: 0.0372
Epoch 1/2... Discriminator Loss: 3.1833... Generator Loss: 0.0829
Epoch 1/2... Discriminator Loss: 2.5118... Generator Loss: 0.1851
Epoch 1/2... Discriminator Loss: 1.9772... Generator Loss: 0.3292
Epoch 1/2... Discriminator Loss: 2.1326... Generator Loss: 0.2714
Epoch 1/2... Discriminator Loss: 1.9402... Generator Loss: 0.3285
Epoch 1/2... Discriminator Loss: 2.2775... Generator Loss: 0.2226
Epoch 1/2... Discriminator Loss: 2.0830... Generator Loss: 0.2664
Epoch 1/2... Discriminator Loss: 1.9908... Generator Loss: 0.3322
Epoch 1/2... Discriminator Loss: 2.0986... Generator Loss: 0.2564
Epoch 1/2... Discriminator Loss: 2.2089... Generator Loss: 0.2509
Epoch 1/2... Discriminator Loss: 1.9707... Generator Loss: 0.5706
Epoch 1/2... Discriminator Loss: 2.0297... Generator Loss: 0.3442
Epoch 1/2... Discriminator Loss: 2.1148... Generator Loss: 0.2914
Epoch 1/2... Discriminator Loss: 2.1255... Generator Loss: 0.3235
Epoch 1/2... Discriminator Loss: 1.9731... Generator Loss: 0.3972
Epoch 1/2... Discriminator Loss: 2.1900... Generator Loss: 0.3075
Epoch 1/2... Discriminator Loss: 1.9735... Generator Loss: 0.3417
Epoch 1/2... Discriminator Loss: 1.7577... Generator Loss: 0.5424
Epoch 1/2... Discriminator Loss: 1.6513... Generator Loss: 0.5446
Epoch 1/2... Discriminator Loss: 1.8496... Generator Loss: 0.4115
Epoch 1/2... Discriminator Loss: 1.7466... Generator Loss: 0.4381
Epoch 1/2... Discriminator Loss: 1.6922... Generator Loss: 0.4852
Epoch 1/2... Discriminator Loss: 1.7296... Generator Loss: 0.5259
Epoch 1/2... Discriminator Loss: 1.6350... Generator Loss: 0.5403
Epoch 1/2... Discriminator Loss: 1.6383... Generator Loss: 0.5559
Epoch 1/2... Discriminator Loss: 1.6403... Generator Loss: 0.6744
Epoch 1/2... Discriminator Loss: 1.8142... Generator Loss: 0.3777
Epoch 1/2... Discriminator Loss: 1.6299... Generator Loss: 0.5018
Epoch 1/2... Discriminator Loss: 1.6436... Generator Loss: 0.4959
Epoch 1/2... Discriminator Loss: 1.6798... Generator Loss: 0.5011
Epoch 1/2... Discriminator Loss: 1.7496... Generator Loss: 0.4700
Epoch 1/2... Discriminator Loss: 1.8912... Generator Loss: 0.3573
Epoch 1/2... Discriminator Loss: 1.9315... Generator Loss: 0.3438
Epoch 1/2... Discriminator Loss: 1.5935... Generator Loss: 0.5400
Epoch 1/2... Discriminator Loss: 1.7676... Generator Loss: 0.4540
Epoch 1/2... Discriminator Loss: 1.7410... Generator Loss: 0.4470
Epoch 1/2... Discriminator Loss: 1.6384... Generator Loss: 0.5528
Epoch 1/2... Discriminator Loss: 1.7074... Generator Loss: 0.4587
Epoch 1/2... Discriminator Loss: 1.8859... Generator Loss: 0.3298
Epoch 1/2... Discriminator Loss: 1.7956... Generator Loss: 0.4060
Epoch 1/2... Discriminator Loss: 1.5472... Generator Loss: 0.5680
Epoch 1/2... Discriminator Loss: 1.7329... Generator Loss: 0.4811
Epoch 1/2... Discriminator Loss: 1.8162... Generator Loss: 0.4267
Epoch 1/2... Discriminator Loss: 1.7709... Generator Loss: 0.4306
Epoch 1/2... Discriminator Loss: 1.6772... Generator Loss: 0.5129
Epoch 1/2... Discriminator Loss: 1.5550... Generator Loss: 0.5811
Epoch 1/2... Discriminator Loss: 1.6742... Generator Loss: 0.5331
Epoch 1/2... Discriminator Loss: 1.8563... Generator Loss: 0.3552
Epoch 1/2... Discriminator Loss: 1.7855... Generator Loss: 0.3958
Epoch 1/2... Discriminator Loss: 1.6150... Generator Loss: 0.6161
Epoch 1/2... Discriminator Loss: 1.7590... Generator Loss: 0.4029
Epoch 1/2... Discriminator Loss: 1.6632... Generator Loss: 0.5517
Epoch 1/2... Discriminator Loss: 1.5678... Generator Loss: 0.6453
Epoch 1/2... Discriminator Loss: 1.6927... Generator Loss: 0.4629
Epoch 1/2... Discriminator Loss: 1.6549... Generator Loss: 0.6542
Epoch 1/2... Discriminator Loss: 1.7461... Generator Loss: 0.4183
Epoch 1/2... Discriminator Loss: 1.6626... Generator Loss: 0.5232
Epoch 1/2... Discriminator Loss: 1.4828... Generator Loss: 0.6896
Epoch 1/2... Discriminator Loss: 1.7007... Generator Loss: 0.5080
Epoch 1/2... Discriminator Loss: 1.8934... Generator Loss: 0.3854
Epoch 1/2... Discriminator Loss: 1.6844... Generator Loss: 0.4898
Epoch 1/2... Discriminator Loss: 1.7252... Generator Loss: 0.4340
Epoch 1/2... Discriminator Loss: 1.6234... Generator Loss: 0.5469
Epoch 1/2... Discriminator Loss: 1.6903... Generator Loss: 0.4708
Epoch 1/2... Discriminator Loss: 1.7498... Generator Loss: 0.4510
Epoch 1/2... Discriminator Loss: 1.7056... Generator Loss: 0.7122
Epoch 1/2... Discriminator Loss: 1.5950... Generator Loss: 0.5541
Epoch 1/2... Discriminator Loss: 1.6801... Generator Loss: 0.6244
Epoch 1/2... Discriminator Loss: 1.6930... Generator Loss: 0.6340
Epoch 1/2... Discriminator Loss: 2.0891... Generator Loss: 0.2989
Epoch 1/2... Discriminator Loss: 1.8454... Generator Loss: 0.4245
Epoch 1/2... Discriminator Loss: 1.7033... Generator Loss: 0.6409
Epoch 1/2... Discriminator Loss: 1.7513... Generator Loss: 0.4466
Epoch 1/2... Discriminator Loss: 1.5610... Generator Loss: 0.6893
Epoch 1/2... Discriminator Loss: 1.4276... Generator Loss: 0.8062
Epoch 1/2... Discriminator Loss: 1.7790... Generator Loss: 0.4178
Epoch 1/2... Discriminator Loss: 1.6856... Generator Loss: 0.5795
Epoch 1/2... Discriminator Loss: 1.7501... Generator Loss: 0.5218
Epoch 1/2... Discriminator Loss: 1.9464... Generator Loss: 0.3392
Epoch 1/2... Discriminator Loss: 1.4896... Generator Loss: 0.8497
Epoch 1/2... Discriminator Loss: 1.7277... Generator Loss: 0.5360
Epoch 1/2... Discriminator Loss: 1.7358... Generator Loss: 0.4945
Epoch 1/2... Discriminator Loss: 1.5125... Generator Loss: 0.6572
Epoch 1/2... Discriminator Loss: 1.7320... Generator Loss: 0.4707
Epoch 1/2... Discriminator Loss: 1.7512... Generator Loss: 0.4842
Epoch 1/2... Discriminator Loss: 1.5569... Generator Loss: 0.5883
Epoch 1/2... Discriminator Loss: 1.6819... Generator Loss: 0.4386
Epoch 1/2... Discriminator Loss: 1.7511... Generator Loss: 0.4118
Epoch 1/2... Discriminator Loss: 1.5985... Generator Loss: 0.7529
Epoch 1/2... Discriminator Loss: 1.6304... Generator Loss: 0.5142
Epoch 1/2... Discriminator Loss: 1.5488... Generator Loss: 0.5668
Epoch 1/2... Discriminator Loss: 1.5950... Generator Loss: 0.5378
Epoch 1/2... Discriminator Loss: 1.8618... Generator Loss: 0.3670
Epoch 1/2... Discriminator Loss: 1.6302... Generator Loss: 0.7015
Epoch 1/2... Discriminator Loss: 1.6117... Generator Loss: 0.6622
Epoch 1/2... Discriminator Loss: 1.8013... Generator Loss: 0.5105
Epoch 1/2... Discriminator Loss: 1.4933... Generator Loss: 0.8135
Epoch 1/2... Discriminator Loss: 1.6812... Generator Loss: 0.5285
Epoch 1/2... Discriminator Loss: 1.6455... Generator Loss: 0.4779
Epoch 1/2... Discriminator Loss: 1.6431... Generator Loss: 0.5625
Epoch 1/2... Discriminator Loss: 1.6083... Generator Loss: 0.4959
Epoch 1/2... Discriminator Loss: 1.6763... Generator Loss: 0.4499
Epoch 1/2... Discriminator Loss: 1.6095... Generator Loss: 0.5764
Epoch 1/2... Discriminator Loss: 1.5432... Generator Loss: 0.5677
Epoch 1/2... Discriminator Loss: 1.5358... Generator Loss: 0.6242
Epoch 1/2... Discriminator Loss: 1.5744... Generator Loss: 0.5661
Epoch 1/2... Discriminator Loss: 1.6750... Generator Loss: 0.4830
Epoch 1/2... Discriminator Loss: 1.5805... Generator Loss: 0.5483
Epoch 1/2... Discriminator Loss: 1.7716... Generator Loss: 0.3928
Epoch 1/2... Discriminator Loss: 1.6014... Generator Loss: 0.5412
Epoch 1/2... Discriminator Loss: 1.5228... Generator Loss: 0.6058
Epoch 1/2... Discriminator Loss: 1.6220... Generator Loss: 0.6154
Epoch 1/2... Discriminator Loss: 1.8781... Generator Loss: 0.4292
Epoch 1/2... Discriminator Loss: 1.5750... Generator Loss: 0.6360
Epoch 1/2... Discriminator Loss: 1.5831... Generator Loss: 0.5857
Epoch 1/2... Discriminator Loss: 1.6034... Generator Loss: 0.5075
Epoch 1/2... Discriminator Loss: 1.4755... Generator Loss: 0.8719
Epoch 1/2... Discriminator Loss: 1.6495... Generator Loss: 0.5923
Epoch 1/2... Discriminator Loss: 1.5151... Generator Loss: 0.6964
Epoch 1/2... Discriminator Loss: 1.5434... Generator Loss: 0.6322
Epoch 1/2... Discriminator Loss: 1.4083... Generator Loss: 0.9973
Epoch 1/2... Discriminator Loss: 1.7211... Generator Loss: 0.5129
Epoch 1/2... Discriminator Loss: 1.5166... Generator Loss: 0.6651
Epoch 1/2... Discriminator Loss: 1.5691... Generator Loss: 0.7587
Epoch 1/2... Discriminator Loss: 1.6856... Generator Loss: 0.4881
Epoch 1/2... Discriminator Loss: 1.6408... Generator Loss: 0.5149
Epoch 1/2... Discriminator Loss: 1.4211... Generator Loss: 0.8309
Epoch 1/2... Discriminator Loss: 1.7159... Generator Loss: 0.4696
Epoch 1/2... Discriminator Loss: 1.5590... Generator Loss: 0.6839
Epoch 1/2... Discriminator Loss: 1.3200... Generator Loss: 0.9308
Epoch 1/2... Discriminator Loss: 1.6820... Generator Loss: 0.4814
Epoch 1/2... Discriminator Loss: 1.6009... Generator Loss: 0.5620
Epoch 1/2... Discriminator Loss: 1.4615... Generator Loss: 0.7620
Epoch 1/2... Discriminator Loss: 1.5135... Generator Loss: 0.5627
Epoch 1/2... Discriminator Loss: 1.7760... Generator Loss: 0.5141
Epoch 1/2... Discriminator Loss: 1.6809... Generator Loss: 0.5802
Epoch 1/2... Discriminator Loss: 1.5273... Generator Loss: 0.6050
Epoch 1/2... Discriminator Loss: 1.5378... Generator Loss: 0.6285
Epoch 1/2... Discriminator Loss: 1.6754... Generator Loss: 0.4942
Epoch 1/2... Discriminator Loss: 1.4846... Generator Loss: 0.6079
Epoch 1/2... Discriminator Loss: 1.4648... Generator Loss: 0.6632
Epoch 1/2... Discriminator Loss: 1.6946... Generator Loss: 0.5089
Epoch 1/2... Discriminator Loss: 1.5914... Generator Loss: 0.5628
Epoch 1/2... Discriminator Loss: 1.6895... Generator Loss: 0.5050
Epoch 1/2... Discriminator Loss: 1.6283... Generator Loss: 0.5164
Epoch 1/2... Discriminator Loss: 1.6025... Generator Loss: 0.5157
Epoch 1/2... Discriminator Loss: 1.3583... Generator Loss: 0.7291
Epoch 1/2... Discriminator Loss: 1.4789... Generator Loss: 0.8204
Epoch 1/2... Discriminator Loss: 1.3949... Generator Loss: 0.7718
Epoch 1/2... Discriminator Loss: 1.7973... Generator Loss: 0.3937
Epoch 1/2... Discriminator Loss: 1.5187... Generator Loss: 0.5725
Epoch 1/2... Discriminator Loss: 1.6437... Generator Loss: 0.4871
Epoch 1/2... Discriminator Loss: 1.4717... Generator Loss: 0.6795
Epoch 1/2... Discriminator Loss: 1.6586... Generator Loss: 0.4477
Epoch 1/2... Discriminator Loss: 1.6425... Generator Loss: 0.5223
Epoch 1/2... Discriminator Loss: 1.6585... Generator Loss: 0.6391
Epoch 1/2... Discriminator Loss: 1.5449... Generator Loss: 0.6672
Epoch 1/2... Discriminator Loss: 1.5278... Generator Loss: 0.6301
Epoch 1/2... Discriminator Loss: 1.6507... Generator Loss: 0.4872
Epoch 1/2... Discriminator Loss: 1.5070... Generator Loss: 0.5984
Epoch 1/2... Discriminator Loss: 1.4281... Generator Loss: 0.7517
Epoch 1/2... Discriminator Loss: 1.4833... Generator Loss: 0.7744
Epoch 1/2... Discriminator Loss: 1.4776... Generator Loss: 0.6626
Epoch 1/2... Discriminator Loss: 1.4355... Generator Loss: 0.6406
Epoch 1/2... Discriminator Loss: 1.4729... Generator Loss: 0.7453
Epoch 1/2... Discriminator Loss: 1.5640... Generator Loss: 0.6144
Epoch 1/2... Discriminator Loss: 1.3847... Generator Loss: 0.7698
Epoch 1/2... Discriminator Loss: 1.4915... Generator Loss: 0.7448
Epoch 1/2... Discriminator Loss: 1.5345... Generator Loss: 0.5782
Epoch 1/2... Discriminator Loss: 1.4543... Generator Loss: 0.6510
Epoch 1/2... Discriminator Loss: 1.4486... Generator Loss: 0.6624
Epoch 1/2... Discriminator Loss: 1.5162... Generator Loss: 0.6844
Epoch 1/2... Discriminator Loss: 1.5339... Generator Loss: 0.6193
Epoch 1/2... Discriminator Loss: 1.4708... Generator Loss: 0.6479
Epoch 1/2... Discriminator Loss: 1.4889... Generator Loss: 0.5995
Epoch 1/2... Discriminator Loss: 1.4965... Generator Loss: 0.7666
Epoch 1/2... Discriminator Loss: 1.4676... Generator Loss: 0.7949
Epoch 1/2... Discriminator Loss: 1.5218... Generator Loss: 0.6057
Epoch 1/2... Discriminator Loss: 1.4806... Generator Loss: 0.8224
Epoch 1/2... Discriminator Loss: 1.4757... Generator Loss: 0.5908
Epoch 1/2... Discriminator Loss: 1.4464... Generator Loss: 0.6723
Epoch 1/2... Discriminator Loss: 1.7526... Generator Loss: 0.4306
Epoch 1/2... Discriminator Loss: 1.5652... Generator Loss: 0.5376
Epoch 1/2... Discriminator Loss: 1.6386... Generator Loss: 0.4918
Epoch 1/2... Discriminator Loss: 1.5508... Generator Loss: 0.5409
Epoch 1/2... Discriminator Loss: 1.4043... Generator Loss: 0.6725
Epoch 2/2... Discriminator Loss: 1.3620... Generator Loss: 0.7755
Epoch 2/2... Discriminator Loss: 1.3629... Generator Loss: 0.8342
Epoch 2/2... Discriminator Loss: 1.4136... Generator Loss: 0.7836
Epoch 2/2... Discriminator Loss: 1.4868... Generator Loss: 0.7785
Epoch 2/2... Discriminator Loss: 1.4991... Generator Loss: 0.6095
Epoch 2/2... Discriminator Loss: 1.5962... Generator Loss: 0.5289
Epoch 2/2... Discriminator Loss: 1.4407... Generator Loss: 0.6404
Epoch 2/2... Discriminator Loss: 1.4135... Generator Loss: 0.6576
Epoch 2/2... Discriminator Loss: 1.3649... Generator Loss: 0.7756
Epoch 2/2... Discriminator Loss: 1.8272... Generator Loss: 0.3750
Epoch 2/2... Discriminator Loss: 1.5254... Generator Loss: 0.6099
Epoch 2/2... Discriminator Loss: 1.4129... Generator Loss: 0.7417
Epoch 2/2... Discriminator Loss: 1.4903... Generator Loss: 0.5437
Epoch 2/2... Discriminator Loss: 1.4989... Generator Loss: 0.6686
Epoch 2/2... Discriminator Loss: 1.3972... Generator Loss: 0.7206
Epoch 2/2... Discriminator Loss: 1.6788... Generator Loss: 0.4354
Epoch 2/2... Discriminator Loss: 1.4783... Generator Loss: 0.7457
Epoch 2/2... Discriminator Loss: 1.5717... Generator Loss: 0.5405
Epoch 2/2... Discriminator Loss: 1.3813... Generator Loss: 0.7783
Epoch 2/2... Discriminator Loss: 1.5690... Generator Loss: 0.5089
Epoch 2/2... Discriminator Loss: 1.5920... Generator Loss: 0.5263
Epoch 2/2... Discriminator Loss: 1.6696... Generator Loss: 0.4361
Epoch 2/2... Discriminator Loss: 1.4533... Generator Loss: 0.6161
Epoch 2/2... Discriminator Loss: 1.3828... Generator Loss: 0.9598
Epoch 2/2... Discriminator Loss: 1.4743... Generator Loss: 0.7806
Epoch 2/2... Discriminator Loss: 1.5852... Generator Loss: 0.5273
Epoch 2/2... Discriminator Loss: 1.2944... Generator Loss: 0.9533
Epoch 2/2... Discriminator Loss: 1.8518... Generator Loss: 0.3566
Epoch 2/2... Discriminator Loss: 1.5717... Generator Loss: 0.4735
Epoch 2/2... Discriminator Loss: 1.5500... Generator Loss: 0.5489
Epoch 2/2... Discriminator Loss: 1.5353... Generator Loss: 0.5919
Epoch 2/2... Discriminator Loss: 1.3452... Generator Loss: 0.8012
Epoch 2/2... Discriminator Loss: 1.5315... Generator Loss: 0.6234
Epoch 2/2... Discriminator Loss: 1.4320... Generator Loss: 0.7270
Epoch 2/2... Discriminator Loss: 1.4030... Generator Loss: 0.7472
Epoch 2/2... Discriminator Loss: 1.6740... Generator Loss: 0.4490
Epoch 2/2... Discriminator Loss: 1.3845... Generator Loss: 0.7275
Epoch 2/2... Discriminator Loss: 1.5384... Generator Loss: 0.5776
Epoch 2/2... Discriminator Loss: 1.4824... Generator Loss: 0.5861
Epoch 2/2... Discriminator Loss: 1.3226... Generator Loss: 0.9615
Epoch 2/2... Discriminator Loss: 1.6451... Generator Loss: 0.4631
Epoch 2/2... Discriminator Loss: 1.3482... Generator Loss: 0.7770
Epoch 2/2... Discriminator Loss: 1.5012... Generator Loss: 0.6270
Epoch 2/2... Discriminator Loss: 1.5525... Generator Loss: 0.6220
Epoch 2/2... Discriminator Loss: 1.4715... Generator Loss: 0.6312
Epoch 2/2... Discriminator Loss: 1.5026... Generator Loss: 0.6483
Epoch 2/2... Discriminator Loss: 1.5796... Generator Loss: 0.7127
Epoch 2/2... Discriminator Loss: 1.3158... Generator Loss: 0.7526
Epoch 2/2... Discriminator Loss: 1.4930... Generator Loss: 0.6120
Epoch 2/2... Discriminator Loss: 1.4310... Generator Loss: 0.7532
Epoch 2/2... Discriminator Loss: 1.5193... Generator Loss: 0.5993
Epoch 2/2... Discriminator Loss: 1.3194... Generator Loss: 0.8842
Epoch 2/2... Discriminator Loss: 1.5384... Generator Loss: 0.5159
Epoch 2/2... Discriminator Loss: 1.4417... Generator Loss: 0.8606
Epoch 2/2... Discriminator Loss: 1.3736... Generator Loss: 0.7479
Epoch 2/2... Discriminator Loss: 1.6242... Generator Loss: 0.4979
Epoch 2/2... Discriminator Loss: 1.5515... Generator Loss: 0.5379
Epoch 2/2... Discriminator Loss: 1.5618... Generator Loss: 0.5059
Epoch 2/2... Discriminator Loss: 1.4705... Generator Loss: 0.6089
Epoch 2/2... Discriminator Loss: 1.4370... Generator Loss: 0.7807
Epoch 2/2... Discriminator Loss: 1.5382... Generator Loss: 0.5258
Epoch 2/2... Discriminator Loss: 1.3675... Generator Loss: 0.6949
Epoch 2/2... Discriminator Loss: 1.6452... Generator Loss: 0.4708
Epoch 2/2... Discriminator Loss: 1.6762... Generator Loss: 0.4543
Epoch 2/2... Discriminator Loss: 1.3581... Generator Loss: 0.7964
Epoch 2/2... Discriminator Loss: 1.5659... Generator Loss: 0.5638
Epoch 2/2... Discriminator Loss: 1.7252... Generator Loss: 0.4414
Epoch 2/2... Discriminator Loss: 1.6002... Generator Loss: 0.4766
Epoch 2/2... Discriminator Loss: 1.6411... Generator Loss: 0.5221
Epoch 2/2... Discriminator Loss: 1.3924... Generator Loss: 0.8172
Epoch 2/2... Discriminator Loss: 1.4807... Generator Loss: 0.6046
Epoch 2/2... Discriminator Loss: 1.6482... Generator Loss: 0.4604
Epoch 2/2... Discriminator Loss: 1.5063... Generator Loss: 0.5985
Epoch 2/2... Discriminator Loss: 1.3470... Generator Loss: 0.7177
Epoch 2/2... Discriminator Loss: 1.4865... Generator Loss: 0.5317
Epoch 2/2... Discriminator Loss: 1.3880... Generator Loss: 0.6663
Epoch 2/2... Discriminator Loss: 1.7179... Generator Loss: 0.4630
Epoch 2/2... Discriminator Loss: 1.4450... Generator Loss: 0.7646
Epoch 2/2... Discriminator Loss: 1.5616... Generator Loss: 0.6922
Epoch 2/2... Discriminator Loss: 1.4334... Generator Loss: 0.6100
Epoch 2/2... Discriminator Loss: 1.3567... Generator Loss: 0.8212
Epoch 2/2... Discriminator Loss: 1.4202... Generator Loss: 0.6757
Epoch 2/2... Discriminator Loss: 1.5738... Generator Loss: 0.5139
Epoch 2/2... Discriminator Loss: 1.5352... Generator Loss: 0.5193
Epoch 2/2... Discriminator Loss: 1.9221... Generator Loss: 0.3046
Epoch 2/2... Discriminator Loss: 1.5263... Generator Loss: 0.6465
Epoch 2/2... Discriminator Loss: 1.4592... Generator Loss: 0.5873
Epoch 2/2... Discriminator Loss: 1.5407... Generator Loss: 0.5282
Epoch 2/2... Discriminator Loss: 1.4752... Generator Loss: 0.5687
Epoch 2/2... Discriminator Loss: 1.3790... Generator Loss: 0.6842
Epoch 2/2... Discriminator Loss: 1.3906... Generator Loss: 0.7892
Epoch 2/2... Discriminator Loss: 1.8316... Generator Loss: 0.3766
Epoch 2/2... Discriminator Loss: 1.4713... Generator Loss: 0.9937
Epoch 2/2... Discriminator Loss: 1.6734... Generator Loss: 0.6364
Epoch 2/2... Discriminator Loss: 1.4041... Generator Loss: 0.7234
Epoch 2/2... Discriminator Loss: 1.4137... Generator Loss: 0.6903
Epoch 2/2... Discriminator Loss: 1.4497... Generator Loss: 0.7710
Epoch 2/2... Discriminator Loss: 1.7502... Generator Loss: 0.3750
Epoch 2/2... Discriminator Loss: 1.6130... Generator Loss: 0.6909
Epoch 2/2... Discriminator Loss: 1.3966... Generator Loss: 0.9863
Epoch 2/2... Discriminator Loss: 1.2868... Generator Loss: 0.8084
Epoch 2/2... Discriminator Loss: 1.5412... Generator Loss: 0.5099
Epoch 2/2... Discriminator Loss: 1.5307... Generator Loss: 0.7017
Epoch 2/2... Discriminator Loss: 1.7048... Generator Loss: 0.4431
Epoch 2/2... Discriminator Loss: 1.3891... Generator Loss: 0.7771
Epoch 2/2... Discriminator Loss: 1.7195... Generator Loss: 0.4012
Epoch 2/2... Discriminator Loss: 1.5879... Generator Loss: 0.5391
Epoch 2/2... Discriminator Loss: 1.3554... Generator Loss: 0.7825
Epoch 2/2... Discriminator Loss: 1.5035... Generator Loss: 0.6335
Epoch 2/2... Discriminator Loss: 1.5615... Generator Loss: 0.5297
Epoch 2/2... Discriminator Loss: 1.4686... Generator Loss: 0.7203
Epoch 2/2... Discriminator Loss: 1.5175... Generator Loss: 0.5415
Epoch 2/2... Discriminator Loss: 1.4288... Generator Loss: 0.7115
Epoch 2/2... Discriminator Loss: 1.4024... Generator Loss: 0.9229
Epoch 2/2... Discriminator Loss: 1.5325... Generator Loss: 0.5759
Epoch 2/2... Discriminator Loss: 1.4569... Generator Loss: 0.6170
Epoch 2/2... Discriminator Loss: 1.6599... Generator Loss: 0.4342
Epoch 2/2... Discriminator Loss: 1.5581... Generator Loss: 0.5454
Epoch 2/2... Discriminator Loss: 1.4078... Generator Loss: 0.6779
Epoch 2/2... Discriminator Loss: 1.5826... Generator Loss: 0.4769
Epoch 2/2... Discriminator Loss: 1.5091... Generator Loss: 0.5595
Epoch 2/2... Discriminator Loss: 1.7579... Generator Loss: 0.4096
Epoch 2/2... Discriminator Loss: 1.5813... Generator Loss: 0.5392
Epoch 2/2... Discriminator Loss: 1.3989... Generator Loss: 1.0050
Epoch 2/2... Discriminator Loss: 1.5719... Generator Loss: 0.4999
Epoch 2/2... Discriminator Loss: 1.6200... Generator Loss: 0.6040
Epoch 2/2... Discriminator Loss: 1.6208... Generator Loss: 0.5202
Epoch 2/2... Discriminator Loss: 1.6952... Generator Loss: 0.4374
Epoch 2/2... Discriminator Loss: 1.6470... Generator Loss: 0.4789
Epoch 2/2... Discriminator Loss: 1.6071... Generator Loss: 0.5136
Epoch 2/2... Discriminator Loss: 1.6201... Generator Loss: 0.5152
Epoch 2/2... Discriminator Loss: 1.6059... Generator Loss: 0.5665
Epoch 2/2... Discriminator Loss: 1.3735... Generator Loss: 0.7352
Epoch 2/2... Discriminator Loss: 1.5788... Generator Loss: 0.5046
Epoch 2/2... Discriminator Loss: 1.5708... Generator Loss: 0.6021
Epoch 2/2... Discriminator Loss: 1.6418... Generator Loss: 0.5995
Epoch 2/2... Discriminator Loss: 1.5981... Generator Loss: 0.5222
Epoch 2/2... Discriminator Loss: 1.6127... Generator Loss: 0.4832
Epoch 2/2... Discriminator Loss: 1.3041... Generator Loss: 0.9338
Epoch 2/2... Discriminator Loss: 1.5860... Generator Loss: 0.5026
Epoch 2/2... Discriminator Loss: 1.4546... Generator Loss: 0.9317
Epoch 2/2... Discriminator Loss: 1.4201... Generator Loss: 0.7181
Epoch 2/2... Discriminator Loss: 1.4538... Generator Loss: 0.6166
Epoch 2/2... Discriminator Loss: 1.3633... Generator Loss: 0.8604
Epoch 2/2... Discriminator Loss: 1.4357... Generator Loss: 0.6782
Epoch 2/2... Discriminator Loss: 1.4580... Generator Loss: 0.8009
Epoch 2/2... Discriminator Loss: 1.4465... Generator Loss: 0.7747
Epoch 2/2... Discriminator Loss: 1.4979... Generator Loss: 0.7348
Epoch 2/2... Discriminator Loss: 1.3926... Generator Loss: 0.7220
Epoch 2/2... Discriminator Loss: 1.5869... Generator Loss: 0.5208
Epoch 2/2... Discriminator Loss: 1.5131... Generator Loss: 0.5437
Epoch 2/2... Discriminator Loss: 1.6447... Generator Loss: 0.4271
Epoch 2/2... Discriminator Loss: 1.4802... Generator Loss: 0.6603
Epoch 2/2... Discriminator Loss: 1.5128... Generator Loss: 0.6974
Epoch 2/2... Discriminator Loss: 1.4171... Generator Loss: 0.8058
Epoch 2/2... Discriminator Loss: 1.6273... Generator Loss: 0.5467
Epoch 2/2... Discriminator Loss: 1.6073... Generator Loss: 0.4935
Epoch 2/2... Discriminator Loss: 1.4141... Generator Loss: 0.6345
Epoch 2/2... Discriminator Loss: 1.5309... Generator Loss: 0.5298
Epoch 2/2... Discriminator Loss: 1.2987... Generator Loss: 0.9408
Epoch 2/2... Discriminator Loss: 1.4629... Generator Loss: 0.5711
Epoch 2/2... Discriminator Loss: 1.3993... Generator Loss: 0.8811
Epoch 2/2... Discriminator Loss: 1.5337... Generator Loss: 0.6402
Epoch 2/2... Discriminator Loss: 1.4595... Generator Loss: 0.6779
Epoch 2/2... Discriminator Loss: 1.3653... Generator Loss: 1.0648
Epoch 2/2... Discriminator Loss: 1.5935... Generator Loss: 0.4933
Epoch 2/2... Discriminator Loss: 1.4431... Generator Loss: 0.6171
Epoch 2/2... Discriminator Loss: 1.3768... Generator Loss: 0.6691
Epoch 2/2... Discriminator Loss: 1.4956... Generator Loss: 1.4314
Epoch 2/2... Discriminator Loss: 1.7193... Generator Loss: 0.4151
Epoch 2/2... Discriminator Loss: 1.4277... Generator Loss: 0.7046
Epoch 2/2... Discriminator Loss: 1.5084... Generator Loss: 0.8481
Epoch 2/2... Discriminator Loss: 1.4045... Generator Loss: 0.8021
Epoch 2/2... Discriminator Loss: 1.1491... Generator Loss: 1.0002
Epoch 2/2... Discriminator Loss: 1.3846... Generator Loss: 0.8819
Epoch 2/2... Discriminator Loss: 1.3262... Generator Loss: 0.8036
Epoch 2/2... Discriminator Loss: 1.4994... Generator Loss: 0.6337
Epoch 2/2... Discriminator Loss: 1.2738... Generator Loss: 0.7653
Epoch 2/2... Discriminator Loss: 1.4334... Generator Loss: 0.6693
Epoch 2/2... Discriminator Loss: 1.6253... Generator Loss: 0.5366
Epoch 2/2... Discriminator Loss: 1.7625... Generator Loss: 0.4157
Epoch 2/2... Discriminator Loss: 1.6378... Generator Loss: 0.4831
Epoch 2/2... Discriminator Loss: 1.4357... Generator Loss: 0.6786
Epoch 2/2... Discriminator Loss: 1.4369... Generator Loss: 0.7555
Epoch 2/2... Discriminator Loss: 1.3743... Generator Loss: 0.7403
Epoch 2/2... Discriminator Loss: 1.5447... Generator Loss: 0.5331
Epoch 2/2... Discriminator Loss: 1.4749... Generator Loss: 0.6100
Epoch 2/2... Discriminator Loss: 1.6042... Generator Loss: 0.5671
Complete.

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [25]:
batch_size = 50
z_dim = 28*28*3
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
iamsb.sendMessage(msg)
Using images of size ( 28 ,  28 ,  3 )
Epoch 1/1... Discriminator Loss: 6.3403... Generator Loss: 0.0034
Epoch 1/1... Discriminator Loss: 5.2614... Generator Loss: 0.0109
Epoch 1/1... Discriminator Loss: 4.3930... Generator Loss: 0.0241
Epoch 1/1... Discriminator Loss: 4.6205... Generator Loss: 0.0194
Epoch 1/1... Discriminator Loss: 4.1353... Generator Loss: 0.0312
Epoch 1/1... Discriminator Loss: 3.6509... Generator Loss: 0.0520
Epoch 1/1... Discriminator Loss: 3.0017... Generator Loss: 0.1044
Epoch 1/1... Discriminator Loss: 3.1295... Generator Loss: 0.1037
Epoch 1/1... Discriminator Loss: 2.7777... Generator Loss: 0.1465
Epoch 1/1... Discriminator Loss: 2.4962... Generator Loss: 0.1981
Epoch 1/1... Discriminator Loss: 2.2510... Generator Loss: 0.2689
Epoch 1/1... Discriminator Loss: 2.1111... Generator Loss: 0.3182
Epoch 1/1... Discriminator Loss: 2.4176... Generator Loss: 0.2054
Epoch 1/1... Discriminator Loss: 2.2063... Generator Loss: 0.2721
Epoch 1/1... Discriminator Loss: 2.0184... Generator Loss: 0.3074
Epoch 1/1... Discriminator Loss: 2.0896... Generator Loss: 0.3546
Epoch 1/1... Discriminator Loss: 2.3237... Generator Loss: 0.3313
Epoch 1/1... Discriminator Loss: 2.1147... Generator Loss: 0.2979
Epoch 1/1... Discriminator Loss: 2.2838... Generator Loss: 0.2702
Epoch 1/1... Discriminator Loss: 1.9990... Generator Loss: 0.4584
Epoch 1/1... Discriminator Loss: 2.7023... Generator Loss: 0.1688
Epoch 1/1... Discriminator Loss: 2.6195... Generator Loss: 0.1686
Epoch 1/1... Discriminator Loss: 2.2953... Generator Loss: 0.2440
Epoch 1/1... Discriminator Loss: 1.6262... Generator Loss: 0.5565
Epoch 1/1... Discriminator Loss: 1.7977... Generator Loss: 0.4182
Epoch 1/1... Discriminator Loss: 2.2954... Generator Loss: 0.2549
Epoch 1/1... Discriminator Loss: 1.6203... Generator Loss: 0.5331
Epoch 1/1... Discriminator Loss: 2.3473... Generator Loss: 0.2227
Epoch 1/1... Discriminator Loss: 2.1050... Generator Loss: 0.3103
Epoch 1/1... Discriminator Loss: 2.3170... Generator Loss: 0.2267
Epoch 1/1... Discriminator Loss: 2.0808... Generator Loss: 0.2855
Epoch 1/1... Discriminator Loss: 2.5282... Generator Loss: 0.2088
Epoch 1/1... Discriminator Loss: 2.8458... Generator Loss: 0.1316
Epoch 1/1... Discriminator Loss: 2.2264... Generator Loss: 0.2901
Epoch 1/1... Discriminator Loss: 2.2694... Generator Loss: 0.2401
Epoch 1/1... Discriminator Loss: 2.1088... Generator Loss: 0.2986
Epoch 1/1... Discriminator Loss: 2.3724... Generator Loss: 0.1978
Epoch 1/1... Discriminator Loss: 1.7844... Generator Loss: 0.4223
Epoch 1/1... Discriminator Loss: 1.9992... Generator Loss: 0.3249
Epoch 1/1... Discriminator Loss: 2.2055... Generator Loss: 0.2526
Epoch 1/1... Discriminator Loss: 2.1792... Generator Loss: 0.2296
Epoch 1/1... Discriminator Loss: 1.9460... Generator Loss: 0.3576
Epoch 1/1... Discriminator Loss: 2.0508... Generator Loss: 0.3131
Epoch 1/1... Discriminator Loss: 2.1198... Generator Loss: 0.2521
Epoch 1/1... Discriminator Loss: 2.1573... Generator Loss: 0.2594
Epoch 1/1... Discriminator Loss: 2.4341... Generator Loss: 0.1921
Epoch 1/1... Discriminator Loss: 2.0961... Generator Loss: 0.2809
Epoch 1/1... Discriminator Loss: 1.7410... Generator Loss: 0.4589
Epoch 1/1... Discriminator Loss: 2.3907... Generator Loss: 0.2048
Epoch 1/1... Discriminator Loss: 2.0510... Generator Loss: 0.2811
Epoch 1/1... Discriminator Loss: 2.3663... Generator Loss: 0.1988
Epoch 1/1... Discriminator Loss: 2.1835... Generator Loss: 0.2399
Epoch 1/1... Discriminator Loss: 2.4692... Generator Loss: 0.1713
Epoch 1/1... Discriminator Loss: 2.3178... Generator Loss: 0.2091
Epoch 1/1... Discriminator Loss: 2.3048... Generator Loss: 0.2221
Epoch 1/1... Discriminator Loss: 2.4082... Generator Loss: 0.2036
Epoch 1/1... Discriminator Loss: 1.8851... Generator Loss: 0.4142
Epoch 1/1... Discriminator Loss: 2.4065... Generator Loss: 0.1914
Epoch 1/1... Discriminator Loss: 2.2803... Generator Loss: 0.2818
Epoch 1/1... Discriminator Loss: 1.9102... Generator Loss: 0.3890
Epoch 1/1... Discriminator Loss: 2.0916... Generator Loss: 0.3096
Epoch 1/1... Discriminator Loss: 2.9414... Generator Loss: 0.1093
Epoch 1/1... Discriminator Loss: 1.7671... Generator Loss: 0.4928
Epoch 1/1... Discriminator Loss: 2.3276... Generator Loss: 0.2793
Epoch 1/1... Discriminator Loss: 2.5874... Generator Loss: 0.1584
Epoch 1/1... Discriminator Loss: 2.4058... Generator Loss: 0.2454
Epoch 1/1... Discriminator Loss: 2.4414... Generator Loss: 0.1940
Epoch 1/1... Discriminator Loss: 2.4496... Generator Loss: 0.1942
Epoch 1/1... Discriminator Loss: 2.8457... Generator Loss: 0.1248
Epoch 1/1... Discriminator Loss: 2.7479... Generator Loss: 0.1433
Epoch 1/1... Discriminator Loss: 1.6187... Generator Loss: 0.7009
Epoch 1/1... Discriminator Loss: 2.4087... Generator Loss: 0.2099
Epoch 1/1... Discriminator Loss: 2.3526... Generator Loss: 0.2213
Epoch 1/1... Discriminator Loss: 2.3311... Generator Loss: 0.2361
Epoch 1/1... Discriminator Loss: 2.3782... Generator Loss: 0.2500
Epoch 1/1... Discriminator Loss: 2.2890... Generator Loss: 0.2878
Epoch 1/1... Discriminator Loss: 2.3756... Generator Loss: 0.2062
Epoch 1/1... Discriminator Loss: 2.0812... Generator Loss: 0.3083
Epoch 1/1... Discriminator Loss: 2.1922... Generator Loss: 0.2680
Epoch 1/1... Discriminator Loss: 2.1449... Generator Loss: 0.3140
Epoch 1/1... Discriminator Loss: 2.1674... Generator Loss: 0.3123
Epoch 1/1... Discriminator Loss: 2.1248... Generator Loss: 0.3151
Epoch 1/1... Discriminator Loss: 2.2774... Generator Loss: 0.2460
Epoch 1/1... Discriminator Loss: 2.5357... Generator Loss: 0.1791
Epoch 1/1... Discriminator Loss: 2.3104... Generator Loss: 0.2401
Epoch 1/1... Discriminator Loss: 2.2289... Generator Loss: 0.2955
Epoch 1/1... Discriminator Loss: 2.2592... Generator Loss: 0.2679
Epoch 1/1... Discriminator Loss: 2.2477... Generator Loss: 0.2737
Epoch 1/1... Discriminator Loss: 2.2553... Generator Loss: 0.2832
Epoch 1/1... Discriminator Loss: 2.2195... Generator Loss: 0.2846
Epoch 1/1... Discriminator Loss: 2.1249... Generator Loss: 0.3274
Epoch 1/1... Discriminator Loss: 2.2607... Generator Loss: 0.2770
Epoch 1/1... Discriminator Loss: 2.4047... Generator Loss: 0.2105
Epoch 1/1... Discriminator Loss: 2.2507... Generator Loss: 0.2860
Epoch 1/1... Discriminator Loss: 2.1432... Generator Loss: 0.3110
Epoch 1/1... Discriminator Loss: 2.3251... Generator Loss: 0.2946
Epoch 1/1... Discriminator Loss: 2.0737... Generator Loss: 0.4348
Epoch 1/1... Discriminator Loss: 2.2824... Generator Loss: 0.2750
Epoch 1/1... Discriminator Loss: 2.2062... Generator Loss: 0.3177
Epoch 1/1... Discriminator Loss: 2.1661... Generator Loss: 0.3240
Epoch 1/1... Discriminator Loss: 2.0880... Generator Loss: 0.4170
Epoch 1/1... Discriminator Loss: 2.3507... Generator Loss: 0.2448
Epoch 1/1... Discriminator Loss: 2.2197... Generator Loss: 0.2699
Epoch 1/1... Discriminator Loss: 2.1226... Generator Loss: 0.3337
Epoch 1/1... Discriminator Loss: 2.2574... Generator Loss: 0.2715
Epoch 1/1... Discriminator Loss: 2.3531... Generator Loss: 0.2282
Epoch 1/1... Discriminator Loss: 2.3004... Generator Loss: 0.2559
Epoch 1/1... Discriminator Loss: 2.3146... Generator Loss: 0.2492
Epoch 1/1... Discriminator Loss: 2.0859... Generator Loss: 0.4247
Epoch 1/1... Discriminator Loss: 2.1510... Generator Loss: 0.4590
Epoch 1/1... Discriminator Loss: 2.2806... Generator Loss: 0.2468
Epoch 1/1... Discriminator Loss: 2.4900... Generator Loss: 0.2286
Epoch 1/1... Discriminator Loss: 2.2253... Generator Loss: 0.4282
Epoch 1/1... Discriminator Loss: 2.3028... Generator Loss: 0.3247
Epoch 1/1... Discriminator Loss: 2.0722... Generator Loss: 0.4115
Epoch 1/1... Discriminator Loss: 2.1749... Generator Loss: 0.2755
Epoch 1/1... Discriminator Loss: 1.9865... Generator Loss: 0.3921
Epoch 1/1... Discriminator Loss: 2.2273... Generator Loss: 0.3206
Epoch 1/1... Discriminator Loss: 2.4104... Generator Loss: 0.2305
Epoch 1/1... Discriminator Loss: 2.0504... Generator Loss: 0.4028
Epoch 1/1... Discriminator Loss: 2.1907... Generator Loss: 0.2728
Epoch 1/1... Discriminator Loss: 1.9507... Generator Loss: 0.4924
Epoch 1/1... Discriminator Loss: 2.1979... Generator Loss: 0.3804
Epoch 1/1... Discriminator Loss: 2.1368... Generator Loss: 0.3006
Epoch 1/1... Discriminator Loss: 2.1906... Generator Loss: 0.2693
Epoch 1/1... Discriminator Loss: 2.1430... Generator Loss: 0.3204
Epoch 1/1... Discriminator Loss: 2.2994... Generator Loss: 0.2449
Epoch 1/1... Discriminator Loss: 2.1524... Generator Loss: 0.3128
Epoch 1/1... Discriminator Loss: 2.1451... Generator Loss: 0.3914
Epoch 1/1... Discriminator Loss: 1.9791... Generator Loss: 0.4534
Epoch 1/1... Discriminator Loss: 2.1237... Generator Loss: 0.3549
Epoch 1/1... Discriminator Loss: 2.3967... Generator Loss: 0.2169
Epoch 1/1... Discriminator Loss: 2.4039... Generator Loss: 0.2971
Epoch 1/1... Discriminator Loss: 2.1668... Generator Loss: 0.2804
Epoch 1/1... Discriminator Loss: 2.1795... Generator Loss: 0.3239
Epoch 1/1... Discriminator Loss: 2.1475... Generator Loss: 0.3230
Epoch 1/1... Discriminator Loss: 2.2297... Generator Loss: 0.2733
Epoch 1/1... Discriminator Loss: 2.0649... Generator Loss: 0.4222
Epoch 1/1... Discriminator Loss: 2.1504... Generator Loss: 0.3344
Epoch 1/1... Discriminator Loss: 2.1538... Generator Loss: 0.3144
Epoch 1/1... Discriminator Loss: 2.1341... Generator Loss: 0.3192
Epoch 1/1... Discriminator Loss: 2.1561... Generator Loss: 0.3085
Epoch 1/1... Discriminator Loss: 2.2250... Generator Loss: 0.2979
Epoch 1/1... Discriminator Loss: 2.1508... Generator Loss: 0.3186
Epoch 1/1... Discriminator Loss: 2.3747... Generator Loss: 0.2515
Epoch 1/1... Discriminator Loss: 2.0580... Generator Loss: 0.3840
Epoch 1/1... Discriminator Loss: 1.9928... Generator Loss: 0.3515
Epoch 1/1... Discriminator Loss: 2.2101... Generator Loss: 0.2763
Epoch 1/1... Discriminator Loss: 2.1938... Generator Loss: 0.3065
Epoch 1/1... Discriminator Loss: 2.2496... Generator Loss: 0.2884
Epoch 1/1... Discriminator Loss: 2.1564... Generator Loss: 0.3456
Epoch 1/1... Discriminator Loss: 2.3311... Generator Loss: 0.2193
Epoch 1/1... Discriminator Loss: 2.0411... Generator Loss: 0.3965
Epoch 1/1... Discriminator Loss: 2.1048... Generator Loss: 0.3185
Epoch 1/1... Discriminator Loss: 2.0759... Generator Loss: 0.3589
Epoch 1/1... Discriminator Loss: 2.2449... Generator Loss: 0.3146
Epoch 1/1... Discriminator Loss: 2.0995... Generator Loss: 0.3627
Epoch 1/1... Discriminator Loss: 2.3152... Generator Loss: 0.2942
Epoch 1/1... Discriminator Loss: 2.1335... Generator Loss: 0.3181
Epoch 1/1... Discriminator Loss: 2.1946... Generator Loss: 0.3392
Epoch 1/1... Discriminator Loss: 2.1631... Generator Loss: 0.3247
Epoch 1/1... Discriminator Loss: 2.3040... Generator Loss: 0.2673
Epoch 1/1... Discriminator Loss: 2.2253... Generator Loss: 0.2509
Epoch 1/1... Discriminator Loss: 1.9467... Generator Loss: 0.4469
Epoch 1/1... Discriminator Loss: 2.2738... Generator Loss: 0.2555
Epoch 1/1... Discriminator Loss: 2.4000... Generator Loss: 0.1966
Epoch 1/1... Discriminator Loss: 2.1795... Generator Loss: 0.3568
Epoch 1/1... Discriminator Loss: 2.1122... Generator Loss: 0.4009
Epoch 1/1... Discriminator Loss: 2.0221... Generator Loss: 0.3815
Epoch 1/1... Discriminator Loss: 2.1531... Generator Loss: 0.2815
Epoch 1/1... Discriminator Loss: 2.2665... Generator Loss: 0.2866
Epoch 1/1... Discriminator Loss: 2.1047... Generator Loss: 0.3279
Epoch 1/1... Discriminator Loss: 2.2419... Generator Loss: 0.2587
Epoch 1/1... Discriminator Loss: 2.0674... Generator Loss: 0.3630
Epoch 1/1... Discriminator Loss: 2.0929... Generator Loss: 0.3913
Epoch 1/1... Discriminator Loss: 2.0756... Generator Loss: 0.3519
Epoch 1/1... Discriminator Loss: 2.1227... Generator Loss: 0.4125
Epoch 1/1... Discriminator Loss: 2.3987... Generator Loss: 0.1932
Epoch 1/1... Discriminator Loss: 2.2716... Generator Loss: 0.2848
Epoch 1/1... Discriminator Loss: 2.3193... Generator Loss: 0.2568
Epoch 1/1... Discriminator Loss: 2.2641... Generator Loss: 0.2858
Epoch 1/1... Discriminator Loss: 2.2326... Generator Loss: 0.2660
Epoch 1/1... Discriminator Loss: 2.0511... Generator Loss: 0.3949
Epoch 1/1... Discriminator Loss: 2.1912... Generator Loss: 0.2886
Epoch 1/1... Discriminator Loss: 2.2735... Generator Loss: 0.2947
Epoch 1/1... Discriminator Loss: 2.2042... Generator Loss: 0.2632
Epoch 1/1... Discriminator Loss: 2.2272... Generator Loss: 0.2825
Epoch 1/1... Discriminator Loss: 2.2981... Generator Loss: 0.2386
Epoch 1/1... Discriminator Loss: 1.9519... Generator Loss: 0.4827
Epoch 1/1... Discriminator Loss: 2.0914... Generator Loss: 0.3510
Epoch 1/1... Discriminator Loss: 2.1412... Generator Loss: 0.2942
Epoch 1/1... Discriminator Loss: 2.2408... Generator Loss: 0.2708
Epoch 1/1... Discriminator Loss: 2.2248... Generator Loss: 0.2798
Epoch 1/1... Discriminator Loss: 2.2564... Generator Loss: 0.2841
Epoch 1/1... Discriminator Loss: 2.2763... Generator Loss: 0.2481
Epoch 1/1... Discriminator Loss: 2.1868... Generator Loss: 0.3477
Epoch 1/1... Discriminator Loss: 2.3309... Generator Loss: 0.2492
Epoch 1/1... Discriminator Loss: 2.4346... Generator Loss: 0.2062
Epoch 1/1... Discriminator Loss: 2.3280... Generator Loss: 0.2802
Epoch 1/1... Discriminator Loss: 2.2330... Generator Loss: 0.2967
Epoch 1/1... Discriminator Loss: 2.2869... Generator Loss: 0.2646
Epoch 1/1... Discriminator Loss: 1.9532... Generator Loss: 0.4001
Epoch 1/1... Discriminator Loss: 2.1591... Generator Loss: 0.2871
Epoch 1/1... Discriminator Loss: 2.1480... Generator Loss: 0.3301
Epoch 1/1... Discriminator Loss: 2.3100... Generator Loss: 0.2296
Epoch 1/1... Discriminator Loss: 2.1382... Generator Loss: 0.2888
Epoch 1/1... Discriminator Loss: 2.0294... Generator Loss: 0.3962
Epoch 1/1... Discriminator Loss: 2.2284... Generator Loss: 0.2644
Epoch 1/1... Discriminator Loss: 2.1899... Generator Loss: 0.2959
Epoch 1/1... Discriminator Loss: 2.0087... Generator Loss: 0.4902
Epoch 1/1... Discriminator Loss: 2.3332... Generator Loss: 0.2687
Epoch 1/1... Discriminator Loss: 2.2155... Generator Loss: 0.2777
Epoch 1/1... Discriminator Loss: 2.1847... Generator Loss: 0.2741
Epoch 1/1... Discriminator Loss: 2.3981... Generator Loss: 0.2090
Epoch 1/1... Discriminator Loss: 2.2231... Generator Loss: 0.2658
Epoch 1/1... Discriminator Loss: 2.2320... Generator Loss: 0.2681
Epoch 1/1... Discriminator Loss: 2.2478... Generator Loss: 0.3291
Epoch 1/1... Discriminator Loss: 1.9723... Generator Loss: 0.4342
Epoch 1/1... Discriminator Loss: 2.2760... Generator Loss: 0.2414
Epoch 1/1... Discriminator Loss: 2.2230... Generator Loss: 0.2932
Epoch 1/1... Discriminator Loss: 2.0326... Generator Loss: 0.4367
Epoch 1/1... Discriminator Loss: 2.2092... Generator Loss: 0.2959
Epoch 1/1... Discriminator Loss: 2.2776... Generator Loss: 0.2763
Epoch 1/1... Discriminator Loss: 2.1139... Generator Loss: 0.3128
Epoch 1/1... Discriminator Loss: 2.0219... Generator Loss: 0.3909
Epoch 1/1... Discriminator Loss: 2.1302... Generator Loss: 0.3180
Epoch 1/1... Discriminator Loss: 2.0647... Generator Loss: 0.3685
Epoch 1/1... Discriminator Loss: 2.3226... Generator Loss: 0.2312
Epoch 1/1... Discriminator Loss: 2.2058... Generator Loss: 0.2851
Epoch 1/1... Discriminator Loss: 2.0519... Generator Loss: 0.4253
Epoch 1/1... Discriminator Loss: 1.9610... Generator Loss: 0.4028
Epoch 1/1... Discriminator Loss: 2.1372... Generator Loss: 0.3294
Epoch 1/1... Discriminator Loss: 2.1805... Generator Loss: 0.2840
Epoch 1/1... Discriminator Loss: 2.2025... Generator Loss: 0.2802
Epoch 1/1... Discriminator Loss: 2.1588... Generator Loss: 0.2797
Epoch 1/1... Discriminator Loss: 2.1091... Generator Loss: 0.3091
Epoch 1/1... Discriminator Loss: 2.2575... Generator Loss: 0.2652
Epoch 1/1... Discriminator Loss: 2.4297... Generator Loss: 0.2129
Epoch 1/1... Discriminator Loss: 2.1903... Generator Loss: 0.3498
Epoch 1/1... Discriminator Loss: 2.3052... Generator Loss: 0.2706
Epoch 1/1... Discriminator Loss: 2.2060... Generator Loss: 0.2800
Epoch 1/1... Discriminator Loss: 2.0454... Generator Loss: 0.3669
Epoch 1/1... Discriminator Loss: 2.0350... Generator Loss: 0.4054
Epoch 1/1... Discriminator Loss: 2.2133... Generator Loss: 0.2669
Epoch 1/1... Discriminator Loss: 2.2748... Generator Loss: 0.2752
Epoch 1/1... Discriminator Loss: 2.1971... Generator Loss: 0.2780
Epoch 1/1... Discriminator Loss: 2.2186... Generator Loss: 0.3081
Epoch 1/1... Discriminator Loss: 2.4660... Generator Loss: 0.2406
Epoch 1/1... Discriminator Loss: 1.9551... Generator Loss: 0.3806
Epoch 1/1... Discriminator Loss: 2.2564... Generator Loss: 0.2756
Epoch 1/1... Discriminator Loss: 2.0643... Generator Loss: 0.3194
Epoch 1/1... Discriminator Loss: 2.1904... Generator Loss: 0.3022
Epoch 1/1... Discriminator Loss: 2.1775... Generator Loss: 0.2702
Epoch 1/1... Discriminator Loss: 2.2876... Generator Loss: 0.2791
Epoch 1/1... Discriminator Loss: 2.0924... Generator Loss: 0.3589
Epoch 1/1... Discriminator Loss: 2.2523... Generator Loss: 0.2991
Epoch 1/1... Discriminator Loss: 2.1015... Generator Loss: 0.3513
Epoch 1/1... Discriminator Loss: 2.2098... Generator Loss: 0.2697
Epoch 1/1... Discriminator Loss: 2.0771... Generator Loss: 0.3591
Epoch 1/1... Discriminator Loss: 2.0953... Generator Loss: 0.3097
Epoch 1/1... Discriminator Loss: 2.1604... Generator Loss: 0.2627
Epoch 1/1... Discriminator Loss: 2.0691... Generator Loss: 0.3953
Epoch 1/1... Discriminator Loss: 2.1668... Generator Loss: 0.2807
Epoch 1/1... Discriminator Loss: 2.2429... Generator Loss: 0.2409
Epoch 1/1... Discriminator Loss: 2.1776... Generator Loss: 0.2666
Epoch 1/1... Discriminator Loss: 2.0440... Generator Loss: 0.3970
Epoch 1/1... Discriminator Loss: 2.2793... Generator Loss: 0.3009
Epoch 1/1... Discriminator Loss: 2.1312... Generator Loss: 0.2971
Epoch 1/1... Discriminator Loss: 2.1436... Generator Loss: 0.3263
Epoch 1/1... Discriminator Loss: 2.2307... Generator Loss: 0.2664
Epoch 1/1... Discriminator Loss: 2.0102... Generator Loss: 0.3895
Epoch 1/1... Discriminator Loss: 2.0872... Generator Loss: 0.3120
Epoch 1/1... Discriminator Loss: 2.1973... Generator Loss: 0.3180
Epoch 1/1... Discriminator Loss: 2.0205... Generator Loss: 0.3520
Epoch 1/1... Discriminator Loss: 2.1060... Generator Loss: 0.3192
Epoch 1/1... Discriminator Loss: 2.1451... Generator Loss: 0.3025
Epoch 1/1... Discriminator Loss: 2.1213... Generator Loss: 0.3468
Epoch 1/1... Discriminator Loss: 2.1675... Generator Loss: 0.3071
Epoch 1/1... Discriminator Loss: 2.2177... Generator Loss: 0.2435
Epoch 1/1... Discriminator Loss: 2.2346... Generator Loss: 0.2499
Epoch 1/1... Discriminator Loss: 2.1723... Generator Loss: 0.3198
Epoch 1/1... Discriminator Loss: 2.1116... Generator Loss: 0.3484
Epoch 1/1... Discriminator Loss: 2.0769... Generator Loss: 0.3584
Epoch 1/1... Discriminator Loss: 2.2614... Generator Loss: 0.3324
Epoch 1/1... Discriminator Loss: 2.0990... Generator Loss: 0.3677
Epoch 1/1... Discriminator Loss: 2.1355... Generator Loss: 0.3509
Epoch 1/1... Discriminator Loss: 2.0729... Generator Loss: 0.3295
Epoch 1/1... Discriminator Loss: 2.0451... Generator Loss: 0.3264
Epoch 1/1... Discriminator Loss: 2.0874... Generator Loss: 0.3318
Epoch 1/1... Discriminator Loss: 2.2438... Generator Loss: 0.2614
Epoch 1/1... Discriminator Loss: 2.3908... Generator Loss: 0.2565
Epoch 1/1... Discriminator Loss: 2.2750... Generator Loss: 0.2499
Epoch 1/1... Discriminator Loss: 2.0115... Generator Loss: 0.3613
Epoch 1/1... Discriminator Loss: 2.0452... Generator Loss: 0.3859
Epoch 1/1... Discriminator Loss: 1.9358... Generator Loss: 0.3519
Epoch 1/1... Discriminator Loss: 2.1836... Generator Loss: 0.2745
Epoch 1/1... Discriminator Loss: 2.1672... Generator Loss: 0.2911
Epoch 1/1... Discriminator Loss: 2.0777... Generator Loss: 0.3383
Epoch 1/1... Discriminator Loss: 2.0627... Generator Loss: 0.3615
Epoch 1/1... Discriminator Loss: 2.3034... Generator Loss: 0.2223
Epoch 1/1... Discriminator Loss: 1.9929... Generator Loss: 0.3997
Epoch 1/1... Discriminator Loss: 2.2104... Generator Loss: 0.2573
Epoch 1/1... Discriminator Loss: 2.2387... Generator Loss: 0.2828
Epoch 1/1... Discriminator Loss: 2.0227... Generator Loss: 0.3846
Epoch 1/1... Discriminator Loss: 2.2481... Generator Loss: 0.2638
Epoch 1/1... Discriminator Loss: 2.0030... Generator Loss: 0.3870
Epoch 1/1... Discriminator Loss: 2.1945... Generator Loss: 0.3125
Epoch 1/1... Discriminator Loss: 2.1337... Generator Loss: 0.3287
Epoch 1/1... Discriminator Loss: 2.2094... Generator Loss: 0.2505
Epoch 1/1... Discriminator Loss: 1.9855... Generator Loss: 0.4346
Epoch 1/1... Discriminator Loss: 2.3662... Generator Loss: 0.2790
Epoch 1/1... Discriminator Loss: 2.0267... Generator Loss: 0.3934
Epoch 1/1... Discriminator Loss: 2.0473... Generator Loss: 0.3175
Epoch 1/1... Discriminator Loss: 2.2059... Generator Loss: 0.2856
Epoch 1/1... Discriminator Loss: 2.0735... Generator Loss: 0.4200
Epoch 1/1... Discriminator Loss: 2.0234... Generator Loss: 0.3856
Epoch 1/1... Discriminator Loss: 1.9740... Generator Loss: 0.3929
Epoch 1/1... Discriminator Loss: 2.3311... Generator Loss: 0.2730
Epoch 1/1... Discriminator Loss: 2.1838... Generator Loss: 0.3238
Epoch 1/1... Discriminator Loss: 2.2263... Generator Loss: 0.2763
Epoch 1/1... Discriminator Loss: 1.9505... Generator Loss: 0.4010
Epoch 1/1... Discriminator Loss: 2.2899... Generator Loss: 0.3020
Epoch 1/1... Discriminator Loss: 2.0620... Generator Loss: 0.3262
Epoch 1/1... Discriminator Loss: 2.0367... Generator Loss: 0.3581
Epoch 1/1... Discriminator Loss: 2.3817... Generator Loss: 0.2071
Epoch 1/1... Discriminator Loss: 2.3143... Generator Loss: 0.2613
Epoch 1/1... Discriminator Loss: 1.9837... Generator Loss: 0.4739
Epoch 1/1... Discriminator Loss: 2.1364... Generator Loss: 0.3323
Epoch 1/1... Discriminator Loss: 2.1570... Generator Loss: 0.2846
Epoch 1/1... Discriminator Loss: 2.1530... Generator Loss: 0.2888
Epoch 1/1... Discriminator Loss: 2.1122... Generator Loss: 0.3134
Epoch 1/1... Discriminator Loss: 2.0646... Generator Loss: 0.3267
Epoch 1/1... Discriminator Loss: 2.2739... Generator Loss: 0.2567
Epoch 1/1... Discriminator Loss: 2.3313... Generator Loss: 0.2339
Epoch 1/1... Discriminator Loss: 2.1415... Generator Loss: 0.2994
Epoch 1/1... Discriminator Loss: 2.0856... Generator Loss: 0.3345
Epoch 1/1... Discriminator Loss: 2.0347... Generator Loss: 0.3591
Epoch 1/1... Discriminator Loss: 2.1816... Generator Loss: 0.3183
Epoch 1/1... Discriminator Loss: 2.1319... Generator Loss: 0.3010
Epoch 1/1... Discriminator Loss: 2.1919... Generator Loss: 0.2830
Epoch 1/1... Discriminator Loss: 2.1581... Generator Loss: 0.3108
Epoch 1/1... Discriminator Loss: 1.9075... Generator Loss: 0.4895
Epoch 1/1... Discriminator Loss: 2.0889... Generator Loss: 0.3553
Epoch 1/1... Discriminator Loss: 2.1751... Generator Loss: 0.2908
Epoch 1/1... Discriminator Loss: 2.1656... Generator Loss: 0.2781
Epoch 1/1... Discriminator Loss: 2.1763... Generator Loss: 0.2624
Epoch 1/1... Discriminator Loss: 2.4056... Generator Loss: 0.2150
Epoch 1/1... Discriminator Loss: 2.3066... Generator Loss: 0.2275
Epoch 1/1... Discriminator Loss: 2.1547... Generator Loss: 0.2805
Epoch 1/1... Discriminator Loss: 2.2700... Generator Loss: 0.2544
Epoch 1/1... Discriminator Loss: 2.2381... Generator Loss: 0.2873
Epoch 1/1... Discriminator Loss: 1.9940... Generator Loss: 0.4116
Epoch 1/1... Discriminator Loss: 2.0451... Generator Loss: 0.3322
Epoch 1/1... Discriminator Loss: 2.3177... Generator Loss: 0.2274
Epoch 1/1... Discriminator Loss: 2.2807... Generator Loss: 0.2392
Epoch 1/1... Discriminator Loss: 2.2331... Generator Loss: 0.2537
Epoch 1/1... Discriminator Loss: 2.1416... Generator Loss: 0.3430
Epoch 1/1... Discriminator Loss: 2.0978... Generator Loss: 0.3399
Epoch 1/1... Discriminator Loss: 2.1413... Generator Loss: 0.3071
Epoch 1/1... Discriminator Loss: 2.1029... Generator Loss: 0.3343
Epoch 1/1... Discriminator Loss: 2.1954... Generator Loss: 0.3027
Epoch 1/1... Discriminator Loss: 2.2282... Generator Loss: 0.2842
Epoch 1/1... Discriminator Loss: 2.2202... Generator Loss: 0.3261
Epoch 1/1... Discriminator Loss: 2.1648... Generator Loss: 0.3205
Epoch 1/1... Discriminator Loss: 2.0549... Generator Loss: 0.3386
Epoch 1/1... Discriminator Loss: 2.2266... Generator Loss: 0.2918
Epoch 1/1... Discriminator Loss: 2.1516... Generator Loss: 0.3224
Epoch 1/1... Discriminator Loss: 2.0870... Generator Loss: 0.3160
Epoch 1/1... Discriminator Loss: 2.1031... Generator Loss: 0.3174
Epoch 1/1... Discriminator Loss: 2.1599... Generator Loss: 0.3533
Epoch 1/1... Discriminator Loss: 2.1416... Generator Loss: 0.3358
Epoch 1/1... Discriminator Loss: 1.9539... Generator Loss: 0.4270
Epoch 1/1... Discriminator Loss: 2.1538... Generator Loss: 0.2745
Epoch 1/1... Discriminator Loss: 2.2841... Generator Loss: 0.2768
Epoch 1/1... Discriminator Loss: 2.1801... Generator Loss: 0.2706
Epoch 1/1... Discriminator Loss: 1.8670... Generator Loss: 0.4755
Epoch 1/1... Discriminator Loss: 2.1866... Generator Loss: 0.3261
Epoch 1/1... Discriminator Loss: 2.1303... Generator Loss: 0.2933
Epoch 1/1... Discriminator Loss: 2.2138... Generator Loss: 0.2566
Epoch 1/1... Discriminator Loss: 2.2047... Generator Loss: 0.2598
Epoch 1/1... Discriminator Loss: 2.1247... Generator Loss: 0.3136
Epoch 1/1... Discriminator Loss: 2.1788... Generator Loss: 0.2992
Epoch 1/1... Discriminator Loss: 2.2393... Generator Loss: 0.2409
Epoch 1/1... Discriminator Loss: 2.3430... Generator Loss: 0.2131
Epoch 1/1... Discriminator Loss: 2.1778... Generator Loss: 0.2994
Epoch 1/1... Discriminator Loss: 2.1802... Generator Loss: 0.2866
Epoch 1/1... Discriminator Loss: 2.0873... Generator Loss: 0.3777
Epoch 1/1... Discriminator Loss: 2.1840... Generator Loss: 0.2778
Epoch 1/1... Discriminator Loss: 2.1044... Generator Loss: 0.3772
Epoch 1/1... Discriminator Loss: 2.1596... Generator Loss: 0.2978
Epoch 1/1... Discriminator Loss: 2.2183... Generator Loss: 0.3415
Epoch 1/1... Discriminator Loss: 2.2873... Generator Loss: 0.2406
Epoch 1/1... Discriminator Loss: 2.1050... Generator Loss: 0.3399
Epoch 1/1... Discriminator Loss: 2.0871... Generator Loss: 0.3825
Epoch 1/1... Discriminator Loss: 2.4179... Generator Loss: 0.2438
Epoch 1/1... Discriminator Loss: 2.0496... Generator Loss: 0.3536
Epoch 1/1... Discriminator Loss: 2.0621... Generator Loss: 0.3564
Epoch 1/1... Discriminator Loss: 2.1898... Generator Loss: 0.3218
Epoch 1/1... Discriminator Loss: 2.1223... Generator Loss: 0.2838
Epoch 1/1... Discriminator Loss: 2.2073... Generator Loss: 0.2715
Epoch 1/1... Discriminator Loss: 2.0868... Generator Loss: 0.3294
Epoch 1/1... Discriminator Loss: 2.0962... Generator Loss: 0.3466
Epoch 1/1... Discriminator Loss: 2.2215... Generator Loss: 0.2652
Epoch 1/1... Discriminator Loss: 2.3685... Generator Loss: 0.2244
Epoch 1/1... Discriminator Loss: 2.0792... Generator Loss: 0.3295
Complete.

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.